Cognitive expansion of user acceptance criteria

ABSTRACT

An unexpressed liking and an unexpressed dislike of a user, which are not specified in the user&#39;s preference are determined by cognitive analytics. The unexpressed liking and dislike correspond to a first and second characteristic of items, respectively. In a list, a first item having the first characteristic and available in an inventory is included, which is, and a second item having the second characteristic and also available in the inventory is excluded. Items included in the list are arranged according to the user&#39;s degrees of liking or the items. An item having the first characteristic is determined to be absent from the inventory. Using completed sales information received from a set of retailer systems, an estimated value of a lost sales opportunity produced when the absent item is purchased is computed. The prioritized list and an accommodation offer responsive to the cost of the lost opportunity are presented.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for determining a user's likes or dislikesrelated to a requested product or service. More particularly, thepresent invention relates to a method, system, and computer programproduct for cognitive expansion of user acceptance criteria.

BACKGROUND

Hereinafter, a “user” is a person who wishes to order, consume, request,purchase, desire, or otherwise select an item. An “item”, as usedherein, is any product or service that can be offered to a user. A“retailer”, as used herein, is any supplier or provider of an item to auser.

Cognitive analytics is the process of analyzing available information orknowledge about a subject-matter domain to create, infer, deduce, orderive new information. Information about a domain can take many forms,including but not limited to knowledge repositories and ontologies. Forexample, domain-specific information can take the form of a list ofpreferences, comments, words, phrases, and their equivalents as relateto an item.

Such information can be sourced from any number of data sources, such asthe repositories on the devices associated with various users. A usergenerally selects the form and content of the information.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product. An embodiment includes a method that receives, from aset of users, a corresponding set of preferences. The embodimentdetermines, by using the set of preferences as input for a cognitiveanalysis system, an unexpressed liking and an unexpressed dislike of afirst user, the first user being associated with a first preference fromthe set of preferences, wherein the first preference does not specifythe unexpressed liking and the unexpressed dislike, and wherein theunexpressed liking corresponds to a first characteristic of items, andwherein the unexpressed dislike corresponds to a second characteristicof items. The embodiment forms a list, using a processor and a memory,by including a first item available in an inventory received from aretailer system, the first item having the first characteristic. Theembodiment excludes from the list, a second item available in theinventory, the second item having the second characteristic. Theembodiment arranges, to form a prioritized list, a set of items that areincluded in the list according to a set of corresponding degrees ofliking associated with the set of items. The embodiment determines, thatan item having the first characteristic is absent from the inventory,the item forming an absent item. The embodiment computes, using theprocessor and the memory, using a set of completed sales informationreceived from a set of retailer systems, an estimated value of a salesopportunity produced by the absent item when the absent item ispurchased, the value of the sales opportunity forming a cost of a lostopportunity. The embodiment causes the retailer system to present theprioritized list and an accommodation offer, the accommodation offerbeing responsive to the cost of the lost opportunity.

An embodiment includes a computer program product. The computer programproduct includes one or more computer-readable storage devices, andprogram instructions stored on at least one of the one or more storagedevices.

An embodiment includes a computer system. The computer system includesone or more processors, one or more computer-readable memories, and oneor more computer-readable storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example configuration for cognitiveexpansion of user acceptance criteria in accordance with an illustrativeembodiment; and

FIG. 4 depicts a flowchart of an example process for cognitive expansionof user acceptance criteria in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

Users have preferences, such as a likes and dislikes when it comes toitems. Some likes are overt, i.e., the user knows, expresses, or canexpress, what the user likes or prefers. Similarly, some dislikes areovert, i.e., the user knows, expresses, or can express, what the userdoes not like or prefer.

The illustrative embodiments recognize that some likes and dislikes ofusers are covert, implicit, not readily knowable as the overt likes anddislikes. Furthermore, such covert likes and dislikes may be not readilyknowable to a retailer, the user's own self, or both. For example, whilea user may know that the user likes soup, the user may even know whichbrand of soup the user prefers to buy, the user may not know whether theuser likes or dislikes a store-brand soup or another brand of soup theuser has never previously consumed.

The illustrative embodiments recognize that whether a retailersuccessfully completes a sale of an item to a user depends significantlyon the likes and dislikes of the user. Thus, the illustrativeembodiments recognize that knowing or learning a user's likes anddislikes—both overt and covert—are extremely important in determiningwhether the items offered by a retailer to a user will result in asuccessful sale of the item to the user.

The illustrative embodiments also recognize that often, more than thelikes of the user, a dislike of the user is harder to determine than aliking of the user. Often, a loss of sale is driven by a dislike of theuser. In many cases, the user knows but the retailer does not know whatthe user dislikes. In many other cases, the dislike is unknown, at leastwith a certain degree of certainty, to both the user and the retailer.

A lost sale is a lost opportunity. A lost opportunity includes not justthe sale of one item that is unavailable or disliked, but also the saleof other items as a result of losing the sale of the one item.

The illustrative embodiments recognize that quantification of a lostopportunity is a difficult problem. The difficulty in quantifying a lostopportunity arises at least because either the user never communicatesto the retailer what sales they lost and for what reason, the retailerhas no presently available way of tracking the sales of other items theylost because of losing the sale of one item, or both. For example, if auser prefers one brand of soup, does not find that brand of soup at oneretailer, goes to another retailer for that brand of soup, and ends upcompleting the entire grocery shopping at the other retailer, resultingin the first retailer losing the sale of other items on the user'sgrocery list as a result of not having the desired brand of soup tooffer to the user.

The illustrative embodiments used to describe the invention generallyaddress and solve the above-described problems and other problemsrelated to the selection of items that retailers should offer to users.

An embodiment can be implemented as a software application. Theapplication implementing an embodiment can be configured as amodification of an existing cognitive analysis system, as a separateapplication that operates in conjunction with an existing cognitiveanalysis system, a standalone application, or some combination thereof.

A set of users participate in a system according to an embodiment, andshare their known or overt likes and dislikes with the embodiment. Auser in such a set of users shares with the embodiment a set of zero ormore likings. A liking can be a general non-item-specific liking—e.g., ageneral preference for all items made by manufacturer xyz, or a likingspecific to an item (e.g., tomato soup), a type of item (e.g., cannedsoup), or a classification or category of item (e.g., packaged foods).In a similar manner, a user also shares a set of zero or more dislikes,which can also be general, item-specific, item-type specific,classification specific, and so on.

The embodiment analyzes the demands of the user generally, as well as ata given time and/or place. For example, in one circumstance, theembodiment determines the user's general demand, to wit, a desire orneed for an overtly or covertly liked item while excluding an overtly orcovertly disliked item. A general demand is applicable anytime and atany place regardless of when and where the user might be present at agiven time.

A demand can also be specific to a time, place, or both. For example, ademand can be for a brand of soup when the user is present at a grocerystore. Generally, within the scope of the illustrative embodiments, areference to a demand encompasses a general demand and a demand specificto a time and/or place unless expressly disambiguated where user.

To determine the user's demand, the embodiment processes the overtlyexpresses likes and dislikes of the user and identifies those itemswhose characteristics match the liked and disliked characteristics. Abrand, a color, a vintage, a location of manufacture, a shape, a size, amaterial, a warranty, a feature of an item are some non-limitingexamples of item characteristics. The embodiment retains the items thatinclude a liked characteristic, and omits or excludes the items thatincludes a disliked characteristic.

The embodiment further determines the user's covert or unexpressed likesand dislikes. In some cases, the user provides hints in other ways thatindicate the dislike. For example, a user's shopping pattern may revealthat the user never buys a particular brand of soup even though thatbrand is readily available where the user shops. Such avoidance isindicative of an inherent dislike that the user has not expressed as adislike in the profile. As another example, the user returning aparticular brand of soup, the user foregoing buying soup when theparticular brand of soup is available, the user buying soup only whenthe particular brand of soup is priced for sale with a significantdifferential from its regular price, and other behavioral patterns canindicate hidden, latent, or unexpressed dislike. Certain otherbehavioral patterns can indicate hidden, latent, or unexpressed likingsin a similar manner.

The embodiment analyzes other data about the user, e.g., purchasehistory of the user, user feedback on an item that can be obtained fromthe user, a retailer, or both. To perform this analysis, the embodimentprepares the data for the analysis, and uses a suitable cognitiveanalytics methodology to identify covert likings or dislikes of theuser.

Through such analysis the embodiment determines a set of covert likesand dislikes of the user. The embodiment then selects the items thatinclude a covertly liked characteristic and satisfies a conditiondetermined from the analysis, and omits or excludes the items thatincludes a covertly disliked characteristic.

The embodiment performs a substitution analysis. Substitution analysisdiscovers or reveals a substitute item for a liked item when the likeditem is unavailable, a substitute item for a disliked item when thesubstitute item has a better probability of being liked. To perform thesubstitution analysis, the embodiment analyzes the likes and dislikes ofother participating users. The likes and dislikes of other users aredetermined in the same manner as described above, and cross-referencedwith the likes and dislikes of the user by the embodiment.

When the input include the data of other users, the cognitive analysissystem is able to use the other users' likes and dislikes as domainknowledge, and identifies substitution items with associatedprobabilities of being liked (or disliked) by the user.

In a similar manner, using the profiles and the cognitive analysissystem, the embodiment determines not only the overt likes, covertlikes, overt dislikes, covert dislikes, and substitution possibilities,the embodiment also determines a prioritization. In other words, theembodiment determines not only which item or a substitute item a userwill like (or dislike), but also a priority that the user is likely toassociate with the liking or (or dislike) for that item or thesubstitute item. The embodiment arranges the items or substitutionsretained or selected for inclusion according to their respectivepriorities in a presentation to the user.

The embodiment also uses the output of the analysis, e.g., the overtlikes, covert likes, overt dislikes, covert dislikes, and substitutionpossibilities, and their respective priorities as feedback in a learningloop. For example, through such analysis, the embodiment also identifiesone or more conditions under which the user accepts a substitution,likes an item, dislikes an item, expresses a preference for one likeditem over another, elects to like an otherwise disliked item, elects todislike an otherwise liked item, or some combination thereof. Forexample, one example condition may reveal the tradeoff that a user iswilling to make if a substitution is prices lower than a liked item bygreater than a threshold differential. Another example condition mayreveal that the user may select a substituted item if the distance ortime to get a liked item exceeds a threshold distance or time from whereor when the user is looking for the item.

As one example, the item with the highest degree of liking is listedhighest on the presentation, followed by other items in a decreasingorder of their respective degrees of likings. As another example, anitem with a greater than threshold degree of liking being unavailable ina given area or period, the likelihood of the user liking a substituteincreases, increasing the substitute's position on the list.Accordingly, the position of the substitute improves on the list as aresult of an unavailability or another unacceptable factor associatedwith a liked item.

A retailer maintains a system to track inventory of items that can beoffered and sales information about completed sales transactions. Forexample, a retailer's system includes the various brands of items thatare available to the users, and transaction information containing itemsand quantities purchased by various users.

The embodiment broadcasts the user's profile to the systems of one ormore retailers. The broadcasting is particularly useful when the user'slocation and time of presence can be determined, and a set of retailersapplicable to the time and location can be identified. For example, ifthe user is in London at Piccadilly Circus looking for a hotel room witha preference for a room by the elevator, the broadcasting can be usefulin collecting information from those hoteliers who are located atPiccadilly Circus in London.

The embodiment obtains from a retailer's system the inventory that theretailer plans to offer to the user. For example, if the hotel systemshows availability for ten different types of rooms, and the profileindicates a liking for a room by the elevator, the hotelier's system mayinclude all ten types of rooms in a message to the embodiment, or mayinclude a shortlist of five types of rooms that the hotelier's systemselects for the user based on the user's broadcasted profile.

Once the embodiment receives the inventory information from theretailer's system, the embodiment cross-references the inventoryinformation with the overt likes, covert likes, overt dislikes, covertdislikes, the substitution possibilities, and their prioritization thathave been determined as described earlier. The embodiment selects fromthe received inventory information those items that include acharacteristic of a liked item or a substitution item, and excludesthose items that include a characteristic of a disliked item.

The embodiment additionally recommends or suggests other liked items orsubstitution items that the retailer's system should include ifavailable at the retailer. The embodiment returns to the retailer'ssystem a prioritized list of items to present to the user.

From time to time, the retailer's system also provides to the embodimentsales information, e.g., about completed sales transactions. Theembodiment receives sales information from a set of retailers in asimilar manner. The embodiment analyzes the sales information todetermine whether the user bought a liked item from a differentretailer. The analysis further determines how much additional sales theuser generated at the other retailer when the user bought the liked itemthere instead of the first retailer. This type of analysis over thesales information from multiple retailers can give important insightinto the lost opportunity for the first retailer and quantifies thatlost opportunity.

It is possible that a retailer-provided inventory does not include anitem that is liked by the user. The lack of a liked item creates apossibility of a lost opportunity during the user's visit at theretailer. Together with the prioritized list of liked and substituteitems, with disliked items omitted, the embodiment also provides lostopportunity analysis as a feedback to the retailer. This feedback allowsthe retailer to either make stocking decisions about the missing item,offer a compensatory promotion or a selective discount, or activateanother remedy for the missing liked item for the user.

When a prioritized list is presented to a user, the user generally makesa selection. Implicitly or expressly, the user also chooses not toselect some items on the prioritized list. An item that is not selectedor deselected is indicative of a preference of the user against theitem. The negative preference may be a dislike for the item or itemtype, or the negative preference may be a lesser degree of likingrelative to other items on the list, or may just be a transient ortemporary choice against the item.

An embodiment collects selection data generated in response to theprioritized list presentation. For example, when a retailer's systempresents the prioritized list of offerings to the user, the retailer'ssystem receives the selection inputs that the user provides. Theretailer's system then shares the selections made by the user from thelist. The selections inform an embodiment in two ways—positivereinforcement for the selected items and negative reinforcement for theitems not selected.

The embodiment supplies the positive reinforcement to the cognitiveanalysis system as a positive learning example, whereby the cognitiveanalysis system learns to increase the degree of liking associated witha selected item. In a similar manner, the embodiment supplies thenegative reinforcement to the cognitive analysis system as a negativelearning example, whereby the cognitive analysis system learns todecrease the degree of liking (or increase the dislike) associated withan item that is not selected.

Over time, the cognitive analysis system learns about the overt andcovert likes and dislikes of the user from numerous selections andnon-selections that the user makes. If, from numerous prioritized liststhat include an item or a substitution, a user consistently chooses notto select the item or a substitution, the cognitive analysis systemlearns that the user has a degree of dislike towards the item orsubstitution. Conversely, if, from numerous prioritized lists thatinclude an item or a substitution, a user consistently chooses to selectthe item or a substitution, the cognitive analysis system learns thatthe user has a degree of liking towards the item or substitution.Occasional selection (or non-selection) of the item or substitution donot significantly change the system's learned liking or dislikecorresponding to the item or substitution.

When an embodiment presents the prioritized list of offerings to theuser, the embodiment receives the selection inputs directly from theuser without relying on a retailer's system. The embodiment uses thepositive and negative reinforcements from the directly receivedselections in a similar manner.

A method of an embodiment described herein, when implemented to executeon a device or data processing system, comprises substantial advancementof the functionality of that device or data processing system in bettercustomizing item offerings to a user. For example, presently availablemethods for presenting items to a user depend largely on the retailer'sown information that the retailer has collected about the user fromprevious transactions with the user. Such a narrow view of the user'slikes fails to reveal the covert likes of the user, the overt and covertdislikes of the user, possible substitutions that would correspond tothe overt and covert likes of the user, lost opportunity quantification,or a combination thereof. An embodiment uses a cognitive analysis systemto identify overt and covert likes and dislikes of a user, substitutionpossibilities based on the information obtained from other users andother retailers. The embodiment further provides a suggested prioritizedlist of items from a retailer's inventory that are likely to interestthe user while also taking into account the dislikes of the user. Theembodiment also informs the retailer about any lost opportunity costassociated with a liked item that is missing in the retailer'sinventory. This manner of cognitive expansion of user acceptancecriteria is unavailable in the presently available methods. Thus, asubstantial advancement of such devices or data processing systems byexecuting a method of an embodiment is in improving the sales to andcustomer satisfaction of a user, improving a user's experience byavoiding the presentation of disliked items, while also enablingdecision making about missing inventory items based on an informedestimate of the lost opportunity costs.

The illustrative embodiments are described with respect to certain typesof items, retailers, likes, dislikes, profiles, preferences,substitutions, quantification, analyses, prioritization, devices, dataprocessing systems, environments, components, and applications only asexamples. Any specific manifestations of these and other similarartifacts are not intended to be limiting to the invention. Any suitablemanifestation of these and other similar artifacts can be selectedwithin the scope of the illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner.

Application 105 implements an embodiment described herein. Device 132 isassociated with a user, and stores user profile 134 as described herein.Any number of users with similarly configured devices of various typescan participate with application 105 a manner described herein.Application 105 interacts with a set of retailer systems, e.g., system107 in server 106 of retailer m through system 107A in server 106A ofretailer n.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114 maycouple to network 102 using wired connections, wireless communicationprotocols, or other suitable data connectivity. Clients 110, 112, and114 may be, for example, personal computers or network computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system such as AIX® (AIX is a trademarkof International Business Machines Corporation in the United States andother countries), Microsoft® Windows® (Microsoft and Windows aretrademarks of Microsoft Corporation in the United States and othercountries), Linux® (Linux is a trademark of Linus Torvalds in the UnitedStates and other countries), iOS™ (iOS is a trademark of Cisco Systems,Inc. licensed to Apple Inc. in the United States and in othercountries), or Android™ (Android is a trademark of Google Inc., in theUnited States and in other countries). An object oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromJava™ programs or applications executing on data processing system 200(Java and all Java-based trademarks and logos are trademarks orregistered trademarks of Oracle Corporation and/or its affiliates).

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1,are located on storage devices, such as in the form of code 226A on harddisk drive 226, and may be loaded into at least one of one or morememories, such as main memory 208, for execution by processing unit 206.The processes of the illustrative embodiments may be performed byprocessing unit 206 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 208, read onlymemory 224, or in one or more peripheral devices.

Furthermore, in one case, code 226A may be downloaded over network 201Afrom remote system 201B, where similar code 201C is stored on a storagedevice 201D. in another case, code 226A may be downloaded over network201A to remote system 201B, where downloaded code 201C is stored on astorage device 201D.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

With reference to FIG. 3, this figure depicts a block diagram of anexample configuration for cognitive expansion of user acceptancecriteria in accordance with an illustrative embodiment. Application 302is an example of application 105 in FIG. 1. User profile 304 is anexample of user profile 134 in FIG. 1. Profiles 304A are any number ofprofiles associated with other users, and configured in the manner ofprofile 304 to interact with application 302. Retailer system 306 is anexample of retailer system 107 in FIG. 1. Retailer systems 306A are anynumber of systems associated with other retailers and configured in themanner of system 306 to interact with application 302.

Profile 304 includes set 308 of likes. A like in set 308 is an overtlike expressed and shared by the user. Profile 304 includes set 310 ofdislikes. A dislike in set 310 is an overt dislike expressed and sharedby the user. History 312 includes purchased items, source or retailerfrom where those items were purchased, user-provided comments aboutthose items, returned items, or some combination of these and othersimilarly purposed pieces of information.

The contents of profile 304 can be arranged in any manner suitable to aparticular implementation. Different profiles 304 and 304A can havedifferent arrangements and formats of similar contents. Contents ofprofile 304 are shared with application 302 in the form of profileinformation 314.

Retailer system 306 includes inventory information 316, which includesdescription and characteristics of the items that can be presented to auser by the retailer of system 306. Completed sales information 318 isdata describing sales transactions completed with a set of users,including with the user of profile 304, if any. System 306 shares theinventory information and sales information with application 302 in theform of shared information 320.

Other systems 306A also include inventory information and salesinformation for other corresponding retailers. Other systems 306Asimilarly share their information with application 302.

Component 322 analyzes profile 304 to determine a demand of theassociated user. Component 322 uses a cognitive analysis system (notshown) to perform the analysis.

The demand determined by component 322 can be a general demand or alocation and/or time specific demand. For example, when the locationand/or time of presence of the user at a particular place or retailer isavailable as input (not shown) to application 302, component 322 candetermine a specific demand.

Using a cognitive analysis system (not shown), profile 304, and otherprofiles 304A, component 324 determines possible substitutions accordingto the liked items determined during the demand analysis.

Application 302 broadcasts (326) profile information 314 to system 306and optionally to other systems 306A. In one case, broadcast 326 occursto system 306 when the user of profile 304 is within a predefineddistance of the retailer of system 306, at a predetermined time, or somecombination thereof. In another case, broadcast 326 occurs to system 306and systems 306A from time to time. In another case, broadcast 326occurs when the user of profile 304 is interacting with the retailer ofsystem 306, some other retailer associated with a system in systems306A, or some combination thereof.

Responsive to broadcast 326, from time to time, at some predeterminedtime or interval, or some combination one or more of these factors,system 306 sends information 320 to application 302. For example, wheninformation 320 is responsive to broadcast 326, information 320 includesthe items which system 306 selects to offer to the user of profile 304.As described herein, such items may be all or a subset of items in theinventory of the retailer according to inventory information 316.

Using information 320, component 328 cross-references the demand andsubstitutions computed by components 322 and 324, respectively, with theitems listed in information 320. As an example, component 328 mayexclude from a list an item received in information 320 if acharacteristic of the item matches a disliked characteristic. As anotherexample, component 328 may include in the list an item received ininformation 320 if a characteristic of the item matches a likedcharacteristic or a characteristic of a selected substitution item.

Component 330 prioritizes the list of the included items according tothe respective degrees to which their characteristics are liked by theuser of profile 304. Component 330 outputs prioritized list 332, whichomits the exclusions. Application 302 sends prioritized list 332 tosystem 306.

As described herein, if a lost opportunity is identified, e.g., when theinventory according to information 320 fails to include a liked item,component 334 computes a lost opportunity cost. Application 302 sendsestimated lost opportunity cost 336 to system 306 as well.

Application 302 causes system 306 to present an offering of items to theuser of profile 304. Specifically, by sending prioritized list 332 andoptionally estimate of lost opportunity cost 336 to system 306,application 302 enables system 306 to prepare offer presentation 338.System 306 can send offer presentation 338 to the user of profile 304directly, e.g., via path A. Alternatively, system 306 can send offerpresentation 338 to application 302 and a component (not shown) ofapplication 302 sends offer presentation 338 to the user, e.g., via pathB. In path B, application 302 can combine other offer presentations (notshown) from other retailers together with offer presentation 338, topresent to the user of profile 304.

With reference to FIG. 4, this figure depicts a flowchart of an exampleprocess for cognitive expansion of user acceptance criteria inaccordance with an illustrative embodiment. Process 400 can beimplemented in application 302 in FIG. 3.

The application collects or receives a set of user profiles from a setof users (block 402). The application receives information of completedsales from a set of retailers (block 404).

For a user, the application identifies a demand (block 406). The demandcan be for a liked item, which may be needed by the user, or which theuser may be looking for. The demand is determined using a cognitiveanalysis system, and includes not only overtly or covertly liked itemsbut also substitutions that are possible given the likings of the user.

The application broadcasts the demand to the systems of one or moreretailers (block 408). In some cases, the broadcast may be to certainsystems selected based on the user's location, time of broadcast, orsome combination of these and other considerations.

The application receives inventory information of all or a subset ofinventory from a retailer system that receives the broadcast (block410). The application cross-references the demand determined in block406 with the received inventory information (block 412). The applicationprocesses omission of dislikes, i.e., removes or omits the items withdisliked characteristics, from the inventory information (block 414).

The items remaining from the inventory information have a likedcharacteristic either for a liked item or a substitution. Theapplication arranges the remaining items in a prioritized order, e.g.,in a decreasing order of the degrees of liking associated with the items(block 416). As described herein, the degree of liking can also bedetermined using the cognitive analysis system.

If a liked item, or a liked characteristic is missing from the inventoryinformation, the application also analyzes a lost opportunity costassociated with that missing liked item or characteristic (block 418).The application transmits the suggested prioritized list of offeringsfrom block 416 to the retailer system that provided the inventoryinformation in block 410 (block 420). When computed, the applicationalso provides to the retailer's system the lost opportunity costestimate for the missing item or characteristic (block 422).

The application causes the retailer's system to make an offeringpresentation to the user according to the suggested prioritized list(block 424). The application ends process 400 thereafter.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments forcognitive expansion of user acceptance criteria and other relatedfeatures, functions, or operations. Where an embodiment or a portionthereof is described with respect to a type of device, the computerimplemented method, system or apparatus, the computer program product,or a portion thereof, are adapted or configured for use with a suitableand comparable manifestation of that type of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail), or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructureincluding the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method comprising: receiving, from a set ofusers, a corresponding set of preferences; determining, by analyzing ashopping pattern of a first user using a cognitive analysis system, anunexpressed liking and an unexpressed dislike of the first user, thefirst user being associated with a first preference from the set ofpreferences, wherein the first preference does not specify theunexpressed liking and the unexpressed dislike, and wherein theunexpressed liking corresponds to a first characteristic of items, andwherein the unexpressed dislike corresponds to a second characteristicof items; forming a list, using a processor and a memory, by including afirst item available in an inventory received from a retailer system,the first item having the first characteristic; excluding from the list,a second item available in the inventory, the second item having thesecond characteristic; arranging, to form a prioritized list, a set ofitems that are included in the list according to a set of correspondingdegrees of liking associated with the set of items, wherein an itemamong the set of items with a greater degree of liking than other itemsis listed first on the prioritized list with a remaining set of itemslisted in a decreasing order of liking; determining, that an item havingthe first characteristic is absent from the inventory in response to abroadcast of a profile of the first user, the broadcast including a timeand location of the first user, the item forming an absent item;determining, using the unexpressed liking and the unexpressed dislike ofthe first user, a condition under which the first user accepts asubstitution for the absent item, the condition comprising a pricedifference for the substitution being greater than a threshold pricedifference; computing, using the processor and the memory, using a setof completed sales information received from a set of retailer systems,an estimated value of a sales opportunity produced by the absent itemwhen the absent item is purchased the value of the sales opportunityforming a cost of a lost opportunity; causing the retailer system topresent the prioritized list and an accommodation offer, theaccommodation offer being responsive to the cost of the lost opportunityand the broadcasted profile; and modifying, responsive to receiving aresponse to the accommodation offer, the unexpressed liking, theunexpressed dislike and the condition.
 2. The method of claim 1, whereinthe value of the sales opportunity includes a value of the absent itemand a value of another item purchased with the absent item.
 3. Themethod of claim 1, further comprising: including in the list a thirditem, the third item having a third characteristic that is substitutablefor the first characteristic.
 4. The method of claim 3, furthercomprising: determining the third characteristic from an answer providedby the cognitive analysis system using based on the set of profiles. 5.The method of claim 3, further comprising: determining, using thecognitive analysis system, a condition under which the third item isacceptable as a substitute for the first item, the third item satisfyingthe condition in addition to having the first characteristic, andwherein the condition is a factor of one of (i) a price differentialbetween the first item and the third item, (ii) a distance between afirst location of availability of the first item and a second distanceof availability of the third item, and (iii) a period between a firsttime of availability of the first item and a second time of availabilityof the third item.
 6. The method of claim 1, further comprising:determining, for an item included in the list, a degree of liking byanalyzing in the cognitive analysis system the set of profiles.
 7. Themethod of claim 1, further comprising: receiving information about theinventory from the retailer system, responsive to transmitting to theretailer system the first preference and the unexpressed liking, whereinthe inventory information comprises a subset of all items available foroffering in the retailer system.
 8. The method of claim 1, wherein apreference of a user includes a set of expressed liked characteristicsand a set of expressed disliked characteristics.
 9. The method of claim1, further comprising: receiving, responsive to presenting theprioritized list, a selection input, the selection input selecting thefirst item; increasing, in the cognitive analysis system, a degree ofliking associated with the first item for the first user; determining,from the selection input, that a substitution item offered as a resultof the accommodation offer is not selected; and decreasing, in thecognitive analysis system, a degree of liking associated with thesubstitution item for the first user.
 10. The method of claim 9, furthercomprising: receiving from the retailer system, the selection input,wherein the retailer's system receives the selection input from thefirst user.
 11. The method of claim 1, wherein the method is embodied ina computer program product comprising one or more computer-readablestorage devices and computer-readable program instructions which arestored on the one or more computer-readable tangible storage devices andexecuted by one or more processors.
 12. The method of claim 1, whereinthe method is embodied in a computer system comprising one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable storage devices and program instructions which arestored on the one or more computer-readable storage devices forexecution by the one or more processors via the one or more memories andexecuted by the one or more processors.
 13. A computer usable programproduct comprising one or more computer-readable storage devices, andprogram instructions stored on at least one of the one or more storagedevices, the stored program instructions comprising: programinstructions to receive, from a set of users, a corresponding set ofpreferences; program instructions to determine, by analyzing a shoppingpattern of a first user using a cognitive analysis system, anunexpressed liking and an unexpressed dislike of the first user, thefirst user being associated with a first preference from the set ofpreferences, wherein the first preference does not specify theunexpressed liking and the unexpressed dislike, and wherein theunexpressed liking corresponds to a first characteristic of items, andwherein the unexpressed dislike corresponds to a second characteristicof items; program instructions to form a list, using a processor and amemory, by including a first item available in an inventory receivedfrom a retailer system, the first item having the first characteristic;program instructions to exclude from the list, a second item availablein the inventory, the second item having the second characteristic;program instructions to arrange, to form a prioritized list, a set ofitems that are included in the list according to a set of correspondingdegrees of liking associated with the set of items, wherein an itemamong the set of items with a greater degree of liking than other itemsis listed first on the prioritized list with a remaining set of itemslisted in a decreasing order of liking; program instructions todetermine, that an item having the first characteristic is absent fromthe inventory in response to a broadcast of a profile of the first user,the broadcast including a time and location of the first user, the itemforming an absent item; program instructions to determine, using theunexpressed liking and the unexpressed dislike of the first user, acondition under which the first user accepts a substitution for theabsent item, the condition comprising a price difference for thesubstitution being greater than a threshold price difference; programinstructions to compute, using the processor and the memory, using a setof completed sales information received from a set of retailer systems,an estimated value of a sales opportunity produced by the absent itemwhen the absent item is purchased, the value of the sales opportunityforming a cost of a lost opportunity; program instructions to cause theretailer system to present the prioritized list and an accommodationoffer, the accommodation offer being responsive to the cost of the lostopportunity and the broadcasted profile; and program instructions tomodify, responsive to receiving a response to the accommodation offer,the unexpressed liking, the unexpressed dislike and the condition. 14.The computer usable program product of claim 13, wherein the value ofthe sales opportunity includes a value of the absent item and a value ofanother item purchased with the absent item.
 15. The computer usableprogram product of claim 13, further comprising: program instructions toinclude in the list a third item, the third item having a thirdcharacteristic that is substitutable for the first characteristic. 16.The computer usable program product of claim 15, further comprising:program instructions to determine the third characteristic from ananswer provided by the cognitive analysis system using based on the setof profiles.
 17. The computer usable program product of claim 13,further comprising: program instructions to determine, for an itemincluded in the list, a degree of liking by analyzing in the cognitiveanalysis system the set of profiles.
 18. The computer usable programproduct of claim 13, further comprising: program instructions to receiveinformation about the inventory from the retailer system, responsive totransmitting to the retailer system the first preference and theunexpressed liking, wherein the inventory information comprises a subsetof all items available for offering in the retailer system.
 19. Thecomputer usable program product of claim 13, wherein a preference of auser includes a set of expressed liked characteristics and a set ofexpressed disliked characteristics.
 20. A computer system comprising oneor more processors, one or more computer-readable memories, and one ormore computer-readable storage devices, and program instructions storedon at least one of the one or more storage devices for execution by atleast one of the one or more processors via at least one of the one ormore memories, the stored program instructions comprising: programinstructions to receive, from a set of users, a corresponding set ofpreferences; program instructions to determine, by analyzing a shoppingpattern of a first user using a cognitive analysis system, anunexpressed liking and an unexpressed dislike of the first user, thefirst user being associated with a first preference from the set ofpreferences, wherein the first preference does not specify theunexpressed liking and the unexpressed dislike, and wherein theunexpressed liking corresponds to a first characteristic of items, andwherein the unexpressed dislike corresponds to a second characteristicof items; program instructions to form a list, using a processor and amemory, by including a first item available in an inventory receivedfrom a retailer system, the first item having the first characteristic;program instructions to exclude from the list, a second item availablein the inventory, the second item having the second characteristic;program instructions to arrange, to form a prioritized list, a set ofitems that are included in the list according to a set of correspondingdegrees of liking associated with the set of items, wherein an itemamong the set of items with a greater degree of liking than other itemsis listed first on the prioritized list with a remaining set of itemslisted in a decreasing order of liking; program instructions todetermine, that an item having the first characteristic is absent fromthe inventory in response to a broadcast of a profile of the first user,the broadcast including a time and location of the first user, the itemforming an absent item; program instructions to determine, using theunexpressed liking and the unexpressed dislike of the first user, acondition under which the first user accepts a substitution for theabsent item, the condition comprising a price difference for thesubstitution being greater than a threshold price difference; programinstructions to compute, using the processor and the memory, using a setof completed sales information received from a set of retailer systems,an estimated value of a sales opportunity produced by the absent itemwhen the absent item is purchased, the value of the sales opportunityforming a cost of a lost opportunity; program instructions to cause theretailer system to present the prioritized list and an accommodationoffer, the accommodation offer being responsive to the cost of the lostopportunity and the broadcasted profile; and program instructions tomodify, responsive to receiving a response to the accommodation offer,the unexpressed liking, the unexpressed dislike and the condition.